I-Corps: Translation Potential of a Handheld Standoff Photothermal Spectroscopy System for Real-time Indication of Viral Epidemics

  • Funded by National Science Foundation (NSF)
  • Total publications:0 publications

Grant number: 2449371

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Key facts

  • Disease

    COVID-19, Unspecified
  • Start & end year

    2025
    2026
  • Known Financial Commitments (USD)

    $50,000
  • Funder

    National Science Foundation (NSF)
  • Principal Investigator

    Thomas Thundat
  • Research Location

    United States of America
  • Lead Research Institution

    SUNY at Buffalo
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Diagnostics

  • Special Interest Tags

    N/A

  • Study Type

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

This I-Corps project is focused on the development of an innovative. non-invasive, diagnostic tool for viral infections. The technology is able to provide rapid, accurate, and real-time detection of influenza, respiratory syncytial virus, and COVID-19. The tool's portability ensures that it can be widely distributed, making it accessible in a variety of healthcare settings, including clinics, hospitals, and in remote areas with limited medical infrastructure. By enabling early and precise diagnosis, this tool can improve patient outcomes, reduce the spread of infectious diseases, and alleviate the burden on healthcare systems. Furthermore, its scalability and low manufacturing costs position it as a viable option for mass production and global distribution, addressing urgent public health needs. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a method to determine multiple pathological conditions simultaneously. The solution detects the infrared signatures produced by the resonant excitation of certain molecules using a tunable source. This approach achieves a limit of detection that is orders of magnitude higher than available nanosensors. By applying machine learning techniques to analyze the nanomechanical infrared response profile, multiple pathological conditions can be identified simultaneously. This device is capable of continuous miniaturization, making it portable and affordable for widespread deployment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.